Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors
Abstract The objective of this work is to perform a comparative flow forecast for the Orós basin (Ceará, Brazil) using artificial neural networks (RNA) and k-neighbors re-sampling technique. The models were developed from the historical series of 100 years of hydrometeorological data (sea surface te...
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ftdatacite:10.6084/m9.figshare.14282000.v1 2023-05-15T17:33:14+02:00 Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors Araújo, Carla Beatriz Costa De Filho, Francisco De Assis De Souza Luiz Martins De Araújo Júnior Cleiton Da Silva Silveira 2021 https://dx.doi.org/10.6084/m9.figshare.14282000.v1 https://scielo.figshare.com/articles/dataset/Seasonal_Flow_Forecast_for_the_Or_s_Dam_Cear_Brazil_Using_Neural_Networks_and_the_Resampling_Technique_of_K-neighbors/14282000/1 unknown SciELO journals https://dx.doi.org/10.1590/0102-7786351015 https://dx.doi.org/10.6084/m9.figshare.14282000 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY 40107 Meteorology FOS Earth and related environmental sciences dataset Dataset 2021 ftdatacite https://doi.org/10.6084/m9.figshare.14282000.v1 https://doi.org/10.1590/0102-7786351015 https://doi.org/10.6084/m9.figshare.14282000 2021-11-05T12:55:41Z Abstract The objective of this work is to perform a comparative flow forecast for the Orós basin (Ceará, Brazil) using artificial neural networks (RNA) and k-neighbors re-sampling technique. The models were developed from the historical series of 100 years of hydrometeorological data (sea surface temperature and flows). Both use as predictors the temperatures of the North Atlantic, South Atlantic and Equatorial Pacific oceans, and forecast July in the next year’s rainy season (January to June). The k-neighbors model was elaborated from the identification of the closest neighbor years for the resampling of the approximation, since the RNA model was formulated from the synaptic and bias weights obtained in the training phase of the network. The Nash-Suttcliffe (E) efficiency coefficient, the coefficient of determination (R²), the Taylor diagram (2001) and the coefficient of determination (R²) were used for the validation step. maximum likelihood ratio. For all comparative variables, the neural model presented better values, indicating that this represents more efficiently the behavior of the flows to the reservoir. Dataset North Atlantic DataCite Metadata Store (German National Library of Science and Technology) Nash ENVELOPE(-62.350,-62.350,-74.233,-74.233) Pacific |
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collection |
DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
unknown |
topic |
40107 Meteorology FOS Earth and related environmental sciences |
spellingShingle |
40107 Meteorology FOS Earth and related environmental sciences Araújo, Carla Beatriz Costa De Filho, Francisco De Assis De Souza Luiz Martins De Araújo Júnior Cleiton Da Silva Silveira Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors |
topic_facet |
40107 Meteorology FOS Earth and related environmental sciences |
description |
Abstract The objective of this work is to perform a comparative flow forecast for the Orós basin (Ceará, Brazil) using artificial neural networks (RNA) and k-neighbors re-sampling technique. The models were developed from the historical series of 100 years of hydrometeorological data (sea surface temperature and flows). Both use as predictors the temperatures of the North Atlantic, South Atlantic and Equatorial Pacific oceans, and forecast July in the next year’s rainy season (January to June). The k-neighbors model was elaborated from the identification of the closest neighbor years for the resampling of the approximation, since the RNA model was formulated from the synaptic and bias weights obtained in the training phase of the network. The Nash-Suttcliffe (E) efficiency coefficient, the coefficient of determination (R²), the Taylor diagram (2001) and the coefficient of determination (R²) were used for the validation step. maximum likelihood ratio. For all comparative variables, the neural model presented better values, indicating that this represents more efficiently the behavior of the flows to the reservoir. |
format |
Dataset |
author |
Araújo, Carla Beatriz Costa De Filho, Francisco De Assis De Souza Luiz Martins De Araújo Júnior Cleiton Da Silva Silveira |
author_facet |
Araújo, Carla Beatriz Costa De Filho, Francisco De Assis De Souza Luiz Martins De Araújo Júnior Cleiton Da Silva Silveira |
author_sort |
Araújo, Carla Beatriz Costa De |
title |
Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors |
title_short |
Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors |
title_full |
Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors |
title_fullStr |
Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors |
title_full_unstemmed |
Seasonal Flow Forecast for the Orós Dam (Ceará, Brazil) Using Neural Networks and the Resampling Technique of K-neighbors |
title_sort |
seasonal flow forecast for the orós dam (ceará, brazil) using neural networks and the resampling technique of k-neighbors |
publisher |
SciELO journals |
publishDate |
2021 |
url |
https://dx.doi.org/10.6084/m9.figshare.14282000.v1 https://scielo.figshare.com/articles/dataset/Seasonal_Flow_Forecast_for_the_Or_s_Dam_Cear_Brazil_Using_Neural_Networks_and_the_Resampling_Technique_of_K-neighbors/14282000/1 |
long_lat |
ENVELOPE(-62.350,-62.350,-74.233,-74.233) |
geographic |
Nash Pacific |
geographic_facet |
Nash Pacific |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
https://dx.doi.org/10.1590/0102-7786351015 https://dx.doi.org/10.6084/m9.figshare.14282000 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.14282000.v1 https://doi.org/10.1590/0102-7786351015 https://doi.org/10.6084/m9.figshare.14282000 |
_version_ |
1766131680879312896 |